Szczegóły publikacji
Opis bibliograficzny
Knowledge discovery approach to automated cardiac SPECT diagnosis / Lukasz A. Kurgan, Krzysztof J. Cios, Ryszard TADEUSIEWICZ, Marek OGIELA, Lucy S. Goodenday // Artificial Intelligence in Medicine ; ISSN 0933-3657. — 2001 — vol. 23 iss. 2, s. 149–169. — Bibliogr. s. 168–169. — Publikacja dostępna online od: 2001-09-26
Autorzy (5)
- Kurgan Łukasz A.
- Cios Krzysztof J.
- AGHTadeusiewicz Ryszard
- AGHOgiela Marek
- Goodenday Lucy S.
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 7295 |
|---|---|
| Data dodania do BaDAP | 2002-01-02 |
| Tekst źródłowy | URL |
| DOI | 10.1016/S0933-3657(01)00082-3 |
| Rok publikacji | 2001 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Czasopismo/seria | Artificial Intelligence in Medicine |
Abstract
The paper describes a computerized process of myocardial perfusion diagnosis from cardiac single proton emission computed tomography (SPECT) images using data mining and knowledge discovery approach. We use a six-step knowledge discovery process. A database consisting of 267 cleaned patient SPECT images (about 3000 2D images), accompanied by clinical information and physician interpretation was created first. Then, a new user-friendly algorithm for computerizing the diagnostic process was designed and implemented. SPECT images were processed to extract a set of features, and then explicit rules were generated, using inductive machine learning and heuristic approaches to mimic cardiologist's diagnosis. The system is able to provide a set of computer diagnoses for cardiac SPECT studies, and can be used as a diagnostic tool by a cardiologist. The achieved results are encouraging because of the high correctness of diagnoses.
